@inproceedings{adam-etal-2026-team-faisalm3at,
    title = "Team faisalm3at {S}em{E}val-2026 Task 3: From Standard Regression to Distributional Alignment in Dimensional Sentiment Analysis",
    author = "Adam, Faisal  and
      Aliyu, Lukman  and
      Aji, Sani  and
      Abubakar, Abdulhamid  and
      Shuaibu, Aliyu Rabiu",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.35/",
    pages = "242--246",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper describes our participation in SemEval2026 Task 3: Dimensional Aspect-Based SentimentAnalysis (DimABSA) (Yu et al., 2026). We utilizeda pre-trained DeBERTa-V3 backbone to capturesemantic meaning through disentangled attention.While standard Mean Squared Error (MSE) loss establishes a performance floor, we propose a HybridMSE-CCCLoss to identify distributional relationships that simple regression missed. Our resultsdemonstrate a 54.6{\%} reduction in validation losscompared to the baseline, significantly improvingdetection in high-intensity emotional bins by mitigating the ``regression to the mean'' phenomenon."
}